Predicting Blood Glucose Levels with Organic Neuromorphic Micro‐Networks

Abstract Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results for reliable glucose predictions, offering timely warnings for glucose fluctuations. The translation of these software‐based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality of life for diabetic patients. ANNs are transforming this field, potentially leading to implantable smart prediction devices and ultimately to a fully artificial pancreas. However, this transition presents several challenges, including the need for specialized, compact, lightweight, and low‐power hardware. Organic polymer‐based electronics are a promising solution as they have the ability to implement the behavior of neural networks, operate at low voltage, and possess key attributes like flexibility, stretchability, and biocompatibility. Here, the study focuses on implementing software‐based neural networks for glucose prediction into hardware systems. How to minimize network requirements, downscale the architecture, and integrate the neural network with electrochemical neuromorphic organic devices, meeting the strict demands of smart implants for in‐body computation of glucose prediction is investigated.


Figure S1
. Overview of the input parameters and frequency definitions.The history length (HL) defines the range (blue) of blood glucose history used as the input for the model, here 3 hours of blood glucose history is indicated with respect to current position in time (green arrow).The sampling frequency (SF) defines the number of sampling points selected from the defined history length (black), expressed in one sample every n minutes.Here, for illustrative purposes, sampling period (SF -1 ) is selected to be 30 minutes.Similarly, the prediction frequency (PF) defines the number of predictions made every n minutes (green), the greater the value for n, the sparser the training data becomes, prediction period (PF -1 ) is illustrated for n = 15 minutes.Predictions are made for a specific prediction horizon (PH), namely 30 minutes (yellow), this horizon is fixed in this dispensation.
Figure S2 (previous page).Additional evaluation metrics on the analyses of different combinations of sampling frequency (180 minutes history) and prediction frequency.Mean (5fold cross-validation) (a) RMSE, percentage of (b) accurate predictions, (c) benign errors, (d) erroneous predictions and (e) time lag for each combination of sampling frequency and prediction frequency evaluated on the test sets of all individuals in the OhioTD1M dataset.The CG-EGA evaluation metrics slightly disfavor smaller sampling frequencies, however, the time lag similarly to the RMSE slightly favors small sampling frequencies.A larger prediction frequency does not show to have any advantage over the default of 5 minutes.

Figure S3 .Figure S8 .
Figure S3.Addi-onal evalua-on metrics on the analyses of different two-point history lengths.Mean (5-fold cross-validation) (a) RMSE, percentage of (b) accurate predictions, (c) benign errors, (d) erroneous predictions and (e) time lag for a two point history length of 5, 10, 15, 20 and 30 minutes evaluated on the test sets of all individuals in the OhioTD1M dataset.With a less immediate gradients, blood glucose predictions become smoother favoring the CG-EGA evalutation metrics, in particularly the rate comparison between the true and predicted values.However, conversely the time lag increases as a less immediate gradient contains less information on the most recent blood glucose trends.

Table S1 . Mean RMSE with standard deviation over the individuals of Cohort 2018 for the input and size reduction.
With PF the prediction frequency, SF the sampling frequency, H the history length and S the number of model parameters.

Table S2 . Mean RMSE with standard deviation over the individuals of Cohort 2020 for the input and size reduction.
With PF the prediction frequency, SF the sampling frequency, H the history length and S the number of model parameters.